This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker , the center for all of your data, analytics, and AI. The relationship between analytics and AI is rapidly evolving.
In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. Why is high-quality and accessible data foundational?
In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager. What stages will it have to go through before it becomes “real,” and how will it get there? The AI Product Pipeline. Though this is not an exhaustive list, most AI products pass through these stages.
“Big data is at the foundation of all the megatrends that are happening.” – Chris Lynch, big data expert. We live in a world saturated with data. Zettabytes of data are floating around in our digital universe, just waiting to be analyzed and explored, according to AnalyticsWeek. Wondering which data science book to read?
Blogs Podcasts Whitepapers and Guides Tools and Calculators Webinars Sample Reports The Evolution of the CFO into the Chief Data Storyteller View Insight Now Our Favorite CFO Blogs The Venture CFO Blog Link: [link] Are you looking for blog posts for CFOs by CFOs? The lion of the jungle. Definitely not.
Diversity in data is one of the three defining characteristics of big data — high data variety — along with high data volume and high velocity. We wrote a longer complete version of this article here: “ Busting Bias with More Data Variety ” at the Western Digital DataMakesPossible.com blog site.
The landscape of big data management has been transformed by the rising popularity of open table formats such as Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake. These formats, designed to address the limitations of traditional data storage systems, have become essential in modern data architectures.
Industry analysts who follow the data and analytics industry tell DataKitchen that they are receiving inquiries about “data fabrics” from enterprise clients on a near-daily basis. Gartner included data fabrics in their top ten trends for data and analytics in 2019. What is a Data Fabric?
What makes an effective DataOps Engineer? You might ask what that means. Errors are an inherent part of data analytics. The product for a data engineer is the data set. For an analyst, the product is the analysis that they deliver for a data object. A DataOps Engineer can make test data available on demand.
As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives. The terms hybrid and multi-cloud are often used interchangeably.
In the era of data-driven business, such perspective is critical. EA also enables a better understanding of change, or impact analysis – which is essential considering the agile, data-driven landscape and its state of flux. IT has graduated from a support department to a proactive, value-driving function.
1) What Are Accounting Reports? What Are Accounting Reports? Table of Contents. 2) Why Do You Need Accounting Reports? 3) Types Of Accounting Reports. 4) Accounting Reports Examples. 5) The Role Of Visuals In Accountant Reports. On the basis of every company’s competent management, we can find accounting reports.
When an organization’s data governance and metadata management programs work in harmony, then everything is easier. Data governance is a complex but critical practice. There’s always more data to handle, much of it unstructured; more data sources, like IoT, more points of integration, and more regulatory compliance requirements.
Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. By providing a standardized framework for data representation, open table formats break down datasilos, enhance data quality, and accelerate analytics at scale.
What the heck is Artificial Intelligence? It is actually smarter than what you see above. A rare post today. It looks a little further out into the future than I normally tend to. It attempts to simplify a topic that has more than it’s share of coolness, confusion and complexity. No more theory, we felt it! AI | Now | Global Maxima.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – Geoffrey Moore. And, as a business, if you use your data wisely, you stand to reap great rewards. Data brings a wealth of invaluable insights that could significantly boost the growth and evolution of your business.
In today’s data-rich environment, the challenge isn’t just collecting data but transforming it into actionable insights that drive strategic decisions. For organizations, this means adopting a data-driven approach—one that replaces gut instinct with factual evidence and predictive insights. What is BI Consulting?
Organizations with a solid understanding of data governance (DG) are better equipped to keep pace with the speed of modern business. In this post, the erwin Experts address: What Is Data Governance? Why Is Data Governance Important? What Is Good Data Governance? What Are the Key Benefits of Data Governance?
How Data Literacy Turns Data from a Burden to a Benefit. Today, data literacy is more important than ever. Data is now being used to support business decisions few executives thought they’d be making even six months ago. So, what is data literacy? What Is Data Literacy?
Remote working has revealed the inconsistency and fragility of workflow processes in many data organizations. The data teams share a common objective; to create analytics for the (internal or external) customer. Data Science Workflow – Kubeflow, Python, R. Data Engineering Workflow – Airflow, ETL.
Whatever your sector or niche, if you want to remain adaptable and get one step ahead of the competition, working with the right data-driven tools and utilizing a corporate dashboard is essential. By squeezing every last drop of value from your business’s most valuable data, you will increase your efficiency while boosting your bottom line.
It’s a fitting way to end what has been another big year for the industry. It can help us leverage significant amounts of data to start designing and discovering new solutions to business and societal problems such as those related to sustainability, life sciences, customer care, employee experience and many more.
In this blog post, we’ll look at datasilos, how they emerge and the problems they can cause within an organization. We’ll also discuss some approaches to resolving the silos, and most importantly, why it’s vital to success in the long run. They are contrary to the approach of a data warehouse.
If you’re serious about a data-driven strategy , you’re going to need a data catalog. Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner. This also diminishes the value of data as an asset.
Businesses have never had access to more data than they do today. Because data without intelligence is just noise. Its not that the data doesnt existits that it isnt connected. Instead of operating in silos, organizations gain a unified, real-time view of performance , allowing them to make faster, more informed decisions.
2020 may well go down as the year where what seems impossible today, did become possible tomorrow. Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. But UOB didn’t stop there.
But amidst all the hype and hubbub, what is the real value of a knowledge graph? In this blog series, we will explore specific industries to highlight the impact of knowledge graphs on critical use cases. Before we dig into the details, however, we need to establish what a knowledge graph is.
While that wouldn’t make much sense these days, think about revenue planning, data, and processes. In this blog post, we’ll look at how you connect the dots between Sales Performance Management and xP&A. In this blog post, we’ll look at how you connect the dots between Sales Performance Management and xP&A.
What drew you to work in the cloud space? What drew you to work in the cloud space? So, what do you like most about the cloud? What motivates you to continue in this industry? Data is now one of the biggest assets an organisation holds and reducing silos allows faster and more effective decision making.
Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.
But kill that thought for a moment and marvel at what it actually as and how good it is. Yes, it does summarize data from many reports into one. But think of what's on it for a moment. Dashboards are no longer thoughtfully processed analysis of data relevant to business goals with an included summary of recommended actions.
But what makes a viable digital transformation strategy? Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. Probably not.
Every company is becoming a data company. In Data-Powered Businesses , we dive into the ways that companies of all kinds are digitally transforming to make smarter data-driven decisions, monetize their data, and create companies that will thrive in our current era of Big Data. Adam Murray: Tell us what you do.
In this post: What Is a Technical Architect? What Is a Technical Architect? We previously have discussed the difference between data architecture and EA plus the difference between solutions architecture and EA. That’s not to say that they operate without the enterprise’s overall strategy in mind.
Sometimes it takes a billion-dollar mistake to bring the murkier side of data ethics into sharp focus. Equifax found this out to their own cost in 2017 when they failed to protect the data of almost 150 million users globally. But is this emerging role the silver bullet for all organizations’ ethical data dilemmas moving forward?
Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. They don’t know exactly whatdata they have or even where some of it is.
The more an enterprise wants to know about itself and its business prospects, the more data it needs to collect and analyze. Additionally, the more data it collects and stores, the better its ability to know customers, to find new ones, and to provide more of what they want to buy. pivot quicker when supply chains falter; .
Underlying digital transformation and investment decisions is a precious asset: data. Now more than ever, decision-makers are looking to do more with their data. This is because the majority of IT departments find it near impossible to just ‘ramp up’ data use, and even more difficult to do so at scale.
The Microsoft Power BI team have released a preview Data Lineage feature and it is a good start for organizations who are starting to think about data management. Businesses need a clear line of sight on data asset ownership and stewardship. Data lineage has always been important but there is renewed attention on it.
Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.
We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.
So, what is file storage? But here’s the caveat: storage at the file level can handle only small amounts of data. If you want your data to grow, there is a certain point at which the hierarchy and permissions can get really complex and slow the system. Okay, then what is block storage? And what about object storage?
Whether it’s rapidly rising costs, an inefficient and outdated data infrastructure, or serious gaps in data governance, there are myriad reasons why organizations are struggling to move past adoption and achieve AI at scale in their enterprises. At the core of many of those is the issue of trust, specifically trusted data.
What is data governance and how do you measure success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? But what comes after these parameters are set?
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content